
import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns
import hvplot.pandas
from scipy import stats
from plotnine import *
from plotnine.data import mtcars

import warnings
warnings.filterwarnings('ignore')


#importing and reading the dataset 

mv_ds=pd.read_csv("/Users/Divya Dubey/Downloads/movies.csv")


mv_ds.head(5)


#Shape of the dataset (no of rows and no of columns)

mv_ds.shape

(735388, 20)


#renaming vote_average to imdb_ratings

mv_ds.rename(columns = {'vote_average':'imdb_ratings'}, inplace = True)
mv_ds.head(10)


#Displaying the data type of the dataset attributes 

mv_ds.dtypes

id                        int64
title                    object
genres                   object
original_language        object
overview                 object
popularity              float64
production_companies     object
release_date             object
budget                  float64
revenue                 float64
runtime                 float64
status                   object
tagline                  object
imdb_ratings            float64
vote_count              float64
credits                  object
keywords                 object
poster_path              object
backdrop_path            object
recommendations          object
dtype: object


#Checking for the missing values in the dataset

mv_ds.isna().any()

id                      False
title                    True
genres                   True
original_language       False
overview                 True
popularity              False
production_companies     True
release_date             True
budget                  False
revenue                 False
runtime                  True
status                  False
tagline                  True
imdb_ratings            False
vote_count              False
credits                  True
keywords                 True
poster_path              True
backdrop_path            True
recommendations          True
dtype: bool


#No of the missing values in the dataset

mv_ds.isna().sum()

id                           0
title                        4
genres                  217209
original_language            0
overview                119309
popularity                   0
production_companies    393560
release_date             54900
budget                       0
revenue                      0
runtime                  37082
status                       0
tagline                 626228
imdb_ratings                 0
vote_count                   0
credits                 228937
keywords                523038
poster_path             193438
backdrop_path           511912
recommendations         696916
dtype: int64


#Let's check if there are any movies with same title
mv_ds['title'].duplicated().sum()

159732


#Let's check if there are any movies with same title and same release date

mv_ds[['title','release_date']].duplicated().sum()

75377


# lets get rid of the duplicate movies

mv_ds.drop_duplicates(subset=['title','release_date'], inplace=True)
mv_ds.shape

(660011, 20)


# there are movies that have 0 vote count, so we will consider only those which have at least more than 20 vote counts.

# filtering the movies

mv_ds1 = mv_ds[mv_ds.vote_count >= 20].reset_index()

mv_ds1.isnull().sum()

index                       0
id                          0
title                       0
genres                    178
original_language           0
overview                  467
popularity                  0
production_companies     3236
release_date                2
budget                      0
revenue                     0
runtime                    15
status                      0
tagline                 20581
imdb_ratings                0
vote_count                  0
credits                   643
keywords                 9664
poster_path               130
backdrop_path            2312
recommendations         15338
dtype: int64


mv_ds1.shape

(43834, 21)


#Dropping the columns from the dataset that is not needed

mv_ds1.drop('poster_path', axis=1, inplace=True)
mv_ds1.drop('backdrop_path', axis=1, inplace=True)
mv_ds1.drop('recommendations', axis=1, inplace=True)
mv_ds1.drop('keywords', axis=1, inplace=True)
mv_ds1.drop('tagline', axis=1, inplace=True)
mv_ds1.drop('credits', axis=1, inplace=True)
mv_ds1.drop('overview', axis=1, inplace=True)

mv_ds1.shape

(43834, 14)


mv_ds1.isnull().sum()

index                      0
id                         0
title                      0
genres                   178
original_language          0
popularity                 0
production_companies    3236
release_date               2
budget                     0
revenue                    0
runtime                   15
status                     0
imdb_ratings               0
vote_count                 0
dtype: int64


# We can remove the null values from the dataset where the count is less . so that we don't loose much data
mv_ds1.dropna(axis=0,subset=['release_date','genres','production_companies'],inplace=True)
mv_ds1.shape

(40554, 14)


#Replacing the runtime with the median of the value as the graph is right skewed 

mv_ds1["runtime"].fillna(mv_ds1["runtime"].median(),inplace=True)
mv_ds1.head(5)


#No of the missing values in the dataset

mv_ds1.isna().sum()

index                   0
id                      0
title                   0
genres                  0
original_language       0
popularity              0
production_companies    0
release_date            0
budget                  0
revenue                 0
runtime                 0
status                  0
imdb_ratings            0
vote_count              0
dtype: int64


#Removing the duplicate values in the datset

mv_ds1.drop_duplicates(inplace=True)
mv_ds1.shape

(40554, 14)


# We need to replace the 0's value in runtime with the median of the value

mv_ds1['runtime'] = mv_ds1['runtime'].replace(0,mv_ds1['runtime'].median())


# We need to replace the 0's value in budget and revenue with the mean of the value 

mv_ds1['budget'] = mv_ds1['budget'].replace(0,mv_ds1['budget'].mean())
mv_ds1['revenue'] = mv_ds1['revenue'].replace(0,mv_ds1['revenue'].mean())
mv_ds1.head(10)


#Creating a new column to check the net profit made by the production_companies (revenue-budget) 

mv_ds1["Profit"]=mv_ds1['revenue'].sub(mv_ds1['budget'], axis = 0) 

mv_ds1


#Creating a new column to check the profit percentage made by the company 

mv_ds1['Profit_Percentage']=(mv_ds1['Profit']/mv_ds1['budget'])*100
mv_ds1


#Most of the values for the status is Released we can drop the status column

mv_ds1.drop('status',axis=1,inplace=True)


mv_ds1.shape
mv_ds1.head(5)


#DATA VISUALIZATION

#Displaying Ratings counts

%matplotlib inline
import matplotlib.pyplot as plt

score=mv_ds1["imdb_ratings"]

font = {'fontname':'Arial', 'size':'14'}
title_font = { 'weight' : 'bold','size':'16'}
plt.hist(score, bins=20)
plt.title("Distribution of the IMDB ratings")
plt.show()


# Correlation with heat map

plt.figure(figsize=(8,6))
sns.heatmap(mv_ds1.corr(),annot=True,fmt='.2f', cmap='viridis')

<AxesSubplot:>


#Checking for the movies released year wise 

mv_ds1['year'] = pd.DatetimeIndex(mv_ds1['release_date']).year

(ggplot(mv_ds1)         # defining what data to use
 + aes(x='year')    # defining what variable to use
 + geom_bar(size=20) # defining the type of plot to use
)

<ggplot: (150631231202)>


#Dropping release_date column

mv_ds1.drop('release_date',axis=1,inplace=True)


#Relationship between the imdb ratings and the profit made by the movie 

ggplot(mv_ds1) +\
    aes(x='imdb_ratings', y='Profit') +\
    geom_line() +\
    stat_smooth(colour='green', span=1)

<ggplot: (150631332060)>


#We can see that there is strong corelation between the imdb_score and the profit . 
#The movies with high imdb rating have made more profit

#Lets check Relationship between imdb_ratings and profit percentage

ggplot(mv_ds1) +\
    aes(x='imdb_ratings', y='Profit_Percentage') +\
    geom_line() +\
    stat_smooth(colour='green', span=1)

<ggplot: (150631360558)>


#Top 10 movies based on the profit they made

plt.figure(figsize=(7,6))
mv_ds1 = mv_ds1.sort_values(by ='Profit' , ascending=False)
mv_ds1_new = mv_ds1.head(10)
a=sns.pointplot(mv_ds1_new['Profit'], mv_ds1_new['budget'], hue=mv_ds1_new['title'])
a.set_xticklabels(a.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
plt.show()


#Top 10 movies based on the imdb_ratings

plt.figure(figsize=(10,8))
mv_ds1 = mv_ds1.sort_values(by ='imdb_ratings' , ascending=False)
mv_ds1_new = mv_ds1.head(10)
a=sns.pointplot(mv_ds1_new['id'], mv_ds1_new['imdb_ratings'], hue=mv_ds1_new['title'])
a.set_xticklabels(a.get_xticklabels(), rotation=40, ha="right")
plt.tight_layout()
plt.show()


mv_ds1.head(10)


#Removing the Columns with names

mv_ds1.drop('title', axis=1, inplace=True)
mv_ds1.drop('production_companies', axis=1, inplace=True)


#Remove the linear dependant variables

mv_ds1.drop('Profit', axis=1, inplace=True)
mv_ds1.drop('Profit_Percentage', axis=1, inplace=True)


#Remove the column that is not needed
mv_ds1.drop('popularity', axis=1, inplace=True)
mv_ds1.drop('index', axis=1, inplace=True)
mv_ds1.drop('id', axis=1, inplace=True)


value_counts=mv_ds1["original_language"].value_counts()
print(value_counts)

en    25102
fr     3438
it     1970
ja     1629
es     1465
      ...  
iu        1
se        1
ne        1
qu        1
eo        1
Name: original_language, Length: 84, dtype: int64


vals = value_counts[:1].index
print (vals)
mv_ds1['original_language'] = mv_ds1.original_language.where(mv_ds1.original_language.isin(vals), 'other')

Index(['en'], dtype='object')


mv_ds1["original_language"].value_counts()

en       25102
other    15452
Name: original_language, dtype: int64


#Dropping vote count table

mv_ds1.drop('vote_count', axis=1, inplace=True)


mv_ds1.drop('genres', axis=1, inplace=True)


#Assigning dummies values to string data

mv_ds1 = pd.get_dummies(data = mv_ds1, columns = ['original_language'] , prefix = ['language'])


# We need to categorize the imdb values in the range of 0-4,4-6,6-8 and 8-10 to mark them as the bad,average,good and excellent movies respectively

mv_ds1["imdb_score"]=pd.cut(mv_ds1['imdb_ratings'], bins=[0,4,6,8,10], right=True, labels=False)+1


#Dropping the imdb_ratings column as it is being replaced with the imdb_score values 

mv_ds1.drop('imdb_ratings',axis=1,inplace=True)


mv_ds1.head(5)


mv_ds1.isnull().sum()

budget            0
revenue           0
runtime           0
year              0
language_en       0
language_other    0
imdb_score        0
dtype: int64


mv_ds1.columns

Index(['budget', 'revenue', 'runtime', 'year', 'language_en', 'language_other',
       'imdb_score'],
      dtype='object')


#Splitting the data into training and test data

X=pd.DataFrame(columns=['runtime', 'budget', 'revenue', 'year', 'language_en'], data=mv_ds1)
y=pd.DataFrame(columns=['imdb_score'],data=mv_ds1)


#Create train and test data set
, 
from sklearn.model_selection import train_test_split

X_train, X_test, y_train, y_test=train_test_split(X,y,test_size=0.3,random_state=100)


#Feature scaling

from sklearn.preprocessing import StandardScaler

sc_X = StandardScaler()
X_train = sc_X.fit_transform(X_train)
X_test = sc_X.transform(X_test)


#Models

#KNN Model

from sklearn.neighbors import KNeighborsClassifier
from sklearn.metrics import accuracy_score

knn_model = KNeighborsClassifier()

#Fit the model with train data
knn_model.fit(X_train,y_train)

#Predict the target on train data set
knnpred = knn_model.predict(X_train)
print(knnpred)
train_knn = accuracy_score(y_train, knnpred)
print("\nAccuracy score on train dataset:", train_knn)

from sklearn import metrics

cnf_matrix_train = metrics.confusion_matrix(y_train, knnpred)
print("\nTrain Data Confusin Matrix:\n", cnf_matrix_train)

[2 3 2 ... 3 2 2]

Accuracy score on train dataset: 0.7274456617465741

Train Data Confusin Matrix:
 [[   84   279   168     0]
 [   69  5479  3931     1]
 [   51  2750 15061    12]
 [    1    97   378    26]]


#Predict the target on train data set
knnpred1 = knn_model.predict(X_test)
print(knnpred1)
test_knn = accuracy_score(y_test, knnpred1)
print("\nAccuracy score on test dataset:", test_knn)

from sklearn import metrics

cnf_matrix_test = metrics.confusion_matrix(y_test, knnpred1)
print("\nTest Data Confusin Matrix:\n", cnf_matrix_test)

[3 3 2 ... 3 3 3]

Accuracy score on test dataset: 0.6411605161502425

Test Data Confusin Matrix:
 [[  13  135   80    0]
 [  35 1831 2217    1]
 [  30 1642 5953   12]
 [   0   29  185    4]]


#Random Forest

from sklearn.ensemble import RandomForestClassifier
rfc = RandomForestClassifier()

#Fit the model with train data
rfc.fit(X_train,y_train)

#Predict the target on train data set

rfc_pred = rfc.predict(X_train)
print(rfc_pred)

train_rfc = accuracy_score(y_train, rfc_pred)
print("\nAccuracy score on train dataset:", train_rfc)

from sklearn import metrics

cnf_matrix_rfc = metrics.confusion_matrix(y_train, rfc_pred)
print("\nTrain Data Confusin Matrix:\n", cnf_matrix_rfc)

[2 3 2 ... 3 2 2]

Accuracy score on train dataset: 0.8618733927502026

Train Data Confusin Matrix:
 [[  246   167   118     0]
 [   12  7540  1924     4]
 [   10  1503 16355     6]
 [    0    41   136   325]]


#Predict the target on test data set

rfc_pred1 = rfc.predict(X_test)
print(rfc_pred1)

test_rfc = accuracy_score(y_test, rfc_pred1)
print("\nAccuracy score on test dataset:", test_rfc)

from sklearn import metrics

cnf_matrix_rfc1 = metrics.confusion_matrix(y_test, rfc_pred1)
print("\nTest Data Confusin Matrix:\n", cnf_matrix_rfc1)

[3 3 3 ... 3 3 3]

Accuracy score on test dataset: 0.6375441768718665

Test Data Confusin Matrix:
 [[   6  133   88    1]
 [  29 1783 2265    7]
 [  29 1607 5965   36]
 [   1   25  189    3]]


#Decision Tree

from sklearn.tree import DecisionTreeClassifier
from sklearn.metrics import accuracy_score

dtree = DecisionTreeClassifier()

#Fit the model with train data
dtree.fit(X_train,y_train)

#Predict the target on train data set
dtree_pred = dtree.predict(X_train)
print(dtree_pred)

train_dtree = accuracy_score(y_train, dtree_pred)
print("\nAccuracy score on train dataset:", train_dtree)

from sklearn import metrics

cnf_matrix_dtree = metrics.confusion_matrix(y_train, dtree_pred)
print("\nTrain Data Confusin Matrix:\n", cnf_matrix_dtree)

[2 2 2 ... 3 2 2]

Accuracy score on train dataset: 0.8619086201430232

Train Data Confusin Matrix:
 [[  302   143    86     0]
 [   50  8063  1367     0]
 [   43  2046 15784     1]
 [    1    57   126   318]]


#Predict the target on test data set

dtree_pred1 = dtree.predict(X_test)
print(dtree_pred1)

test_dtree = accuracy_score(y_test, dtree_pred1)
print("\nAccuracy score on test dataset:", test_dtree)

from sklearn import metrics

cnf_matrix_dtree1 = metrics.confusion_matrix(y_test, dtree_pred1)
print("\nTest Data Confusin Matrix:\n", cnf_matrix_dtree1)

[3 3 2 ... 3 3 3]

Accuracy score on test dataset: 0.5935727788279773

Test Data Confusin Matrix:
 [[  13  135   78    2]
 [  86 2007 1977   14]
 [  73 2235 5188  141]
 [   2   46  156   14]]


#Linear Regression

from sklearn.linear_model import LinearRegression


linreg = LinearRegression()

#Fit the model with train data
linreg.fit(X_train,y_train)

#Predict the target on train data set
linreg_pred = linreg.predict(X_train)
print(linreg_pred)

train_linreg = accuracy_score(y_train, linreg_pred.round())

print("\nAccuracy score on train dataset:", train_linreg)

from sklearn import metrics

cnf_matrix_linreg = metrics.confusion_matrix(y_train, linreg_pred.round())
print("\nTrain Data Confusin Matrix:\n", cnf_matrix_linreg)

[[2.5304214 ]
 [2.82431788]
 [2.4951687 ]
 ...
 [2.70776624]
 [2.721521  ]
 [2.6848867 ]]

Accuracy score on train dataset: 0.6079543452989045

Train Data Confusin Matrix:
 [[    0     5   526     0     0]
 [    0   132  9348     0     0]
 [    0   738 17120    16     0]
 [    0    43   452     6     1]
 [    0     0     0     0     0]]


#Predict the target on test data set
linreg_pred1 = linreg.predict(X_test)
print(linreg_pred1)

test_linreg = accuracy_score(y_test, linreg_pred1.round())

print("\nAccuracy score on test dataset:", test_linreg)

from sklearn import metrics

cnf_matrix_linreg1 = metrics.confusion_matrix(y_test, linreg_pred1.round())
print("\nTest Data Confusin Matrix:\n", cnf_matrix_linreg1)

[[2.61446715]
 [2.70317356]
 [2.62081408]
 ...
 [2.77506609]
 [2.55518263]
 [2.69697516]]

Accuracy score on test dataset: 0.6073806197090491

Test Data Confusin Matrix:
 [[   0    1  227    0    0]
 [   0   60 4024    0    0]
 [   0  300 7330    6    1]
 [   0   19  199    0    0]
 [   0    0    0    0    0]]


#Model Comparison

#classification report for train data on all models

from sklearn.metrics import classification_report

print('classification report for train data on all models\n')

print('KNN Reports\n',classification_report(y_train, knnpred))
print('Random Forests Reports\n',classification_report(y_train, rfc_pred))
print('Decision Tree Reports\n',classification_report(y_train, dtree_pred))
print('Linear Regression Reports\n',classification_report(y_train, linreg_pred.round()))

classification report for train data on all models

KNN Reports
               precision    recall  f1-score   support

           1       0.41      0.16      0.23       531
           2       0.64      0.58      0.61      9480
           3       0.77      0.84      0.81     17874
           4       0.67      0.05      0.10       502

    accuracy                           0.73     28387
   macro avg       0.62      0.41      0.43     28387
weighted avg       0.72      0.73      0.72     28387

Random Forests Reports
               precision    recall  f1-score   support

           1       0.92      0.46      0.62       531
           2       0.82      0.80      0.81      9480
           3       0.88      0.92      0.90     17874
           4       0.97      0.65      0.78       502

    accuracy                           0.86     28387
   macro avg       0.90      0.71      0.77     28387
weighted avg       0.86      0.86      0.86     28387

Decision Tree Reports
               precision    recall  f1-score   support

           1       0.76      0.57      0.65       531
           2       0.78      0.85      0.81      9480
           3       0.91      0.88      0.90     17874
           4       1.00      0.63      0.77       502

    accuracy                           0.86     28387
   macro avg       0.86      0.73      0.78     28387
weighted avg       0.87      0.86      0.86     28387

Linear Regression Reports
               precision    recall  f1-score   support

         1.0       0.00      0.00      0.00       531
         2.0       0.14      0.01      0.03      9480
         3.0       0.62      0.96      0.76     17874
         4.0       0.27      0.01      0.02       502
         5.0       0.00      0.00      0.00         0

    accuracy                           0.61     28387
   macro avg       0.21      0.20      0.16     28387
weighted avg       0.45      0.61      0.48     28387


#classification report for test data on all models

from sklearn.metrics import classification_report

print('classification report for test data on all models\n')

print('KNN Reports\n',classification_report(y_test, knnpred1))
print('Random Forests Reports\n',classification_report(y_test, rfc_pred1))
print('Decision Tree Reports\n',classification_report(y_test, dtree_pred1))
print('Linear Regression Reports\n',classification_report(y_test, linreg_pred1.round()))

classification report for test data on all models

KNN Reports
               precision    recall  f1-score   support

           1       0.17      0.06      0.08       228
           2       0.50      0.45      0.47      4084
           3       0.71      0.78      0.74      7637
           4       0.24      0.02      0.03       218

    accuracy                           0.64     12167
   macro avg       0.40      0.33      0.33     12167
weighted avg       0.62      0.64      0.63     12167

Random Forests Reports
               precision    recall  f1-score   support

           1       0.09      0.03      0.04       228
           2       0.50      0.44      0.47      4084
           3       0.70      0.78      0.74      7637
           4       0.06      0.01      0.02       218

    accuracy                           0.64     12167
   macro avg       0.34      0.31      0.32     12167
weighted avg       0.61      0.64      0.62     12167

Decision Tree Reports
               precision    recall  f1-score   support

           1       0.07      0.06      0.06       228
           2       0.45      0.49      0.47      4084
           3       0.70      0.68      0.69      7637
           4       0.08      0.06      0.07       218

    accuracy                           0.59     12167
   macro avg       0.33      0.32      0.32     12167
weighted avg       0.60      0.59      0.59     12167

Linear Regression Reports
               precision    recall  f1-score   support

         1.0       0.00      0.00      0.00       228
         2.0       0.16      0.01      0.03      4084
         3.0       0.62      0.96      0.76      7637
         4.0       0.00      0.00      0.00       218
         5.0       0.00      0.00      0.00         0

    accuracy                           0.61     12167
   macro avg       0.16      0.19      0.16     12167
weighted avg       0.44      0.61      0.48     12167

	id	title	genres	original_language	overview	popularity	production_companies	release_date	budget	revenue	runtime	status	tagline	vote_average	vote_count	credits	keywords	poster_path	backdrop_path	recommendations
0	436270	Black Adam	Action-Fantasy-Science Fiction	en	Nearly 5000 years after he was bestowed with t...	11752.795	New Line Cinema-Flynn Picture Company-Seven Bu...	2022-10-19	200000000.0	368000000.0	125.0	Released	The world needed a hero. It got Black Adam.	7.292	2420.0	Dwayne Johnson-Aldis Hodge-Noah Centineo-Sarah...	lightning-anti hero-superhero-based on comic-d...	/pFlaoHTZeyNkG83vxsAJiGzfSsa.jpg	/bQXAqRx2Fgc46uCVWgoPz5L5Dtr.jpg	663712-49046-642721-963954-365297-887731-63113...
1	724495	The Woman King	Action-Drama-History	en	The story of the Agojie the all-female unit of...	4957.725	TriStar Pictures-Entertainment One-JuVee Produ...	2022-09-15	50000000.0	91000000.0	135.0	Released	Her reign begins.	7.906	586.0	Viola Davis-Thuso Mbedu-Lashana Lynch-John Boy...	africa-arranged marriage-warrior woman-based o...	/438QXt1E3WJWb3PqNniK0tAE5c1.jpg	/7zQJYV02yehWrQN6NjKsBorqUUS.jpg	49046-436270-913290-619730-882598-913816-80093...
2	829799	Paradise City	Crime-Action-Thriller	en	Renegade bounty hunter Ryan Swan must carve hi...	3133.802	Arcana Studio-308 Enterprises-Yale Productions...	2022-11-11	20000000.0	0.0	93.0	Released	NaN	6.153	36.0	John Travolta-Bruce Willis-Blake Jenner-Praya ...	NaN	/uGuHHS9SWv7MrFhCH6zoGGd7DA8.jpg	/au4HUSWDRadIcl9CqySlw1kJMfo.jpg	879444-1018494-1015724-1026706-1032427-945444-...
3	792775	Cop Secret	Comedy-Action-Thriller	is	When Bússi Iceland's toughest cop is forced to...	2447.908	Pegasus Pictures-Stöð 2-SamFilm	2022-05-23	0.0	0.0	100.0	Released	To solve this crime, they'll need to break all...	6.375	28.0	Auðunn Blöndal-Egill Einarsson-Steinunn Ólína ...	co-workers relationship-bank robbery-lgbt-crim...	/jnWyZsaCl3Ke6u6ReSmBRO8S1rX.jpg	/sUuzl04qNIYsnwCLQpZ2RSvXA1V.jpg	639933-852448-823625
4	956101	The Eighth Clause	Thriller	la	Kat and Borja appear to be a perfect couple bu...	2259.303	SDB Films-El Hombre Orquesta	2022-04-29	0.0	0.0	0.0	Released	NaN	4.600	10.0	Maite Perroni-Manuel Vega-Óscar Jaenada-Jessic...	NaN	/8tc8eMFAX2SDC1TRu987qFQy8Cl.jpg	/kLnqNE9Af5QHyvUxw8cDGhF1ilv.jpg	NaN

	id	title	genres	original_language	overview	popularity	production_companies	release_date	budget	revenue	runtime	status	tagline	imdb_ratings	vote_count	credits	keywords	poster_path	backdrop_path	recommendations
0	436270	Black Adam	Action-Fantasy-Science Fiction	en	Nearly 5000 years after he was bestowed with t...	11752.795	New Line Cinema-Flynn Picture Company-Seven Bu...	2022-10-19	200000000.0	368000000.0	125.0	Released	The world needed a hero. It got Black Adam.	7.292	2420.0	Dwayne Johnson-Aldis Hodge-Noah Centineo-Sarah...	lightning-anti hero-superhero-based on comic-d...	/pFlaoHTZeyNkG83vxsAJiGzfSsa.jpg	/bQXAqRx2Fgc46uCVWgoPz5L5Dtr.jpg	663712-49046-642721-963954-365297-887731-63113...
1	724495	The Woman King	Action-Drama-History	en	The story of the Agojie the all-female unit of...	4957.725	TriStar Pictures-Entertainment One-JuVee Produ...	2022-09-15	50000000.0	91000000.0	135.0	Released	Her reign begins.	7.906	586.0	Viola Davis-Thuso Mbedu-Lashana Lynch-John Boy...	africa-arranged marriage-warrior woman-based o...	/438QXt1E3WJWb3PqNniK0tAE5c1.jpg	/7zQJYV02yehWrQN6NjKsBorqUUS.jpg	49046-436270-913290-619730-882598-913816-80093...
2	829799	Paradise City	Crime-Action-Thriller	en	Renegade bounty hunter Ryan Swan must carve hi...	3133.802	Arcana Studio-308 Enterprises-Yale Productions...	2022-11-11	20000000.0	0.0	93.0	Released	NaN	6.153	36.0	John Travolta-Bruce Willis-Blake Jenner-Praya ...	NaN	/uGuHHS9SWv7MrFhCH6zoGGd7DA8.jpg	/au4HUSWDRadIcl9CqySlw1kJMfo.jpg	879444-1018494-1015724-1026706-1032427-945444-...
3	792775	Cop Secret	Comedy-Action-Thriller	is	When Bússi Iceland's toughest cop is forced to...	2447.908	Pegasus Pictures-Stöð 2-SamFilm	2022-05-23	0.0	0.0	100.0	Released	To solve this crime, they'll need to break all...	6.375	28.0	Auðunn Blöndal-Egill Einarsson-Steinunn Ólína ...	co-workers relationship-bank robbery-lgbt-crim...	/jnWyZsaCl3Ke6u6ReSmBRO8S1rX.jpg	/sUuzl04qNIYsnwCLQpZ2RSvXA1V.jpg	639933-852448-823625
4	956101	The Eighth Clause	Thriller	la	Kat and Borja appear to be a perfect couple bu...	2259.303	SDB Films-El Hombre Orquesta	2022-04-29	0.0	0.0	0.0	Released	NaN	4.600	10.0	Maite Perroni-Manuel Vega-Óscar Jaenada-Jessic...	NaN	/8tc8eMFAX2SDC1TRu987qFQy8Cl.jpg	/kLnqNE9Af5QHyvUxw8cDGhF1ilv.jpg	NaN
5	505642	Black Panther: Wakanda Forever	Action-Adventure-Science Fiction	en	Queen Ramonda Shuri M’Baku Okoye and the Dora ...	2248.449	Marvel Studios	2022-11-09	250000000.0	733000000.0	162.0	Released	Forever.	7.547	1182.0	Letitia Wright-Lupita Nyong'o-Danai Gurira-Win...	hero-sequel-superhero-based on comic-duringcre...	/ps2oKfhY6DL3alynlSqY97gHSsg.jpg	/xDMIl84Qo5Tsu62c9DGWhmPI67A.jpg	436270-785084-928123-663712-555604-615952-7441...
6	948276	Lost Bullet 2	Action-Drama-Thriller	fr	Having cleared his name genius mechanic Lino h...	2229.672	Versus Production-Nolita-Inoxy Films	2022-11-10	0.0	0.0	98.0	Released	NaN	6.650	140.0	Alban Lenoir-Stéfi Celma-Pascale Arbillot-Séba...	french film	/uAeZI1JJbLPq7Bu5dziH7emHeu7.jpg	/a64zCJnqOwHYdFHfdQFqQcxYSAz.jpg	NaN
7	899294	Frank and Penelope	Thriller-Horror-Crime	en	A tale of love and violence when a man on his ...	2128.548	NaN	2022-06-03	0.0	0.0	112.0	Released	Prey for love.	7.500	37.0	Kevin Dillon-Sean Patrick Flanery-Johnathon Sc...	NaN	/5NpXoAi3nEQkEgLO09nmotPfyNa.jpg	/eyiSLRh44SKKWIJ6bxWq8z1sscB.jpg	NaN
8	872177	Corrective Measures	Science Fiction-Action	en	Set in San Tiburon the world's most dangerous ...	1940.324	The Exchange-Tubi TV-Arcana Productions	2022-04-29	0.0	0.0	106.0	Released	Anarchy in the world's most dangerous prison.	5.100	35.0	Bruce Willis-Hayley Sales-Michael Rooker-Kat R...	based on comic-lockdown-prison measures	/aHFq9NMhavOL0jtQvmHQ1c5e0ya.jpg	/8Tr79lfoCkOYRg8SYwWit4OoQLi.jpg	1001717-639933-648579-755566-526896-725201
9	774752	The Guardians of the Galaxy Holiday Special	Comedy-Science Fiction-Adventure	en	On a mission to make Christmas unforgettable f...	1916.450	Marvel Studios-Troll Court Entertainment-Kevin...	2022-11-25	0.0	0.0	45.0	Released	The perfect present is a galaxy away.	7.465	564.0	Chris Pratt-Dave Bautista-Karen Gillan-Pom Kle...	holiday-celebrity-superhero-talking dog-saving...	/8dqXyslZ2hv49Oiob9UjlGSHSTR.jpg	/rfnmMYuZ6EKOBvQLp2wqP21v7sI.jpg	1010705-35981-1015724-410113-15997-616820-3666...

	index	id	title	genres	original_language	popularity	production_companies	release_date	budget	revenue	runtime	status	imdb_ratings	vote_count
0	0	436270	Black Adam	Action-Fantasy-Science Fiction	en	11752.795	New Line Cinema-Flynn Picture Company-Seven Bu...	2022-10-19	200000000.0	368000000.0	125.0	Released	7.292	2420.0
1	1	724495	The Woman King	Action-Drama-History	en	4957.725	TriStar Pictures-Entertainment One-JuVee Produ...	2022-09-15	50000000.0	91000000.0	135.0	Released	7.906	586.0
2	2	829799	Paradise City	Crime-Action-Thriller	en	3133.802	Arcana Studio-308 Enterprises-Yale Productions...	2022-11-11	20000000.0	0.0	93.0	Released	6.153	36.0
3	3	792775	Cop Secret	Comedy-Action-Thriller	is	2447.908	Pegasus Pictures-Stöð 2-SamFilm	2022-05-23	0.0	0.0	100.0	Released	6.375	28.0
4	5	505642	Black Panther: Wakanda Forever	Action-Adventure-Science Fiction	en	2248.449	Marvel Studios	2022-11-09	250000000.0	733000000.0	162.0	Released	7.547	1182.0

	index	id	title	genres	original_language	popularity	production_companies	release_date	budget	revenue	runtime	status	imdb_ratings	vote_count
0	0	436270	Black Adam	Action-Fantasy-Science Fiction	en	11752.795	New Line Cinema-Flynn Picture Company-Seven Bu...	2022-10-19	2.000000e+08	3.680000e+08	125.0	Released	7.292	2420.0
1	1	724495	The Woman King	Action-Drama-History	en	4957.725	TriStar Pictures-Entertainment One-JuVee Produ...	2022-09-15	5.000000e+07	9.100000e+07	135.0	Released	7.906	586.0
2	2	829799	Paradise City	Crime-Action-Thriller	en	3133.802	Arcana Studio-308 Enterprises-Yale Productions...	2022-11-11	2.000000e+07	1.647994e+07	93.0	Released	6.153	36.0
3	3	792775	Cop Secret	Comedy-Action-Thriller	is	2447.908	Pegasus Pictures-Stöð 2-SamFilm	2022-05-23	6.066852e+06	1.647994e+07	100.0	Released	6.375	28.0
4	5	505642	Black Panther: Wakanda Forever	Action-Adventure-Science Fiction	en	2248.449	Marvel Studios	2022-11-09	2.500000e+08	7.330000e+08	162.0	Released	7.547	1182.0
5	6	948276	Lost Bullet 2	Action-Drama-Thriller	fr	2229.672	Versus Production-Nolita-Inoxy Films	2022-11-10	6.066852e+06	1.647994e+07	98.0	Released	6.650	140.0
7	8	872177	Corrective Measures	Science Fiction-Action	en	1940.324	The Exchange-Tubi TV-Arcana Productions	2022-04-29	6.066852e+06	1.647994e+07	106.0	Released	5.100	35.0
8	9	774752	The Guardians of the Galaxy Holiday Special	Comedy-Science Fiction-Adventure	en	1916.450	Marvel Studios-Troll Court Entertainment-Kevin...	2022-11-25	6.066852e+06	1.647994e+07	45.0	Released	7.465	564.0
9	10	846778	Margaux	Horror-Science Fiction	en	1751.341	Motion Picture Corporation of America-Lighthou...	2022-09-09	6.066852e+06	1.647994e+07	105.0	Released	6.800	43.0
10	11	830784	Lyle, Lyle, Crocodile	Comedy-Family-Music	en	1710.176	Columbia Pictures-Eagle Pictures-TSG Entertain...	2022-10-07	5.000000e+07	7.976194e+07	106.0	Released	7.810	124.0

	index	id	title	genres	original_language	popularity	production_companies	release_date	budget	revenue	runtime	status	imdb_ratings	vote_count	Profit
0	0	436270	Black Adam	Action-Fantasy-Science Fiction	en	11752.795	New Line Cinema-Flynn Picture Company-Seven Bu...	2022-10-19	2.000000e+08	3.680000e+08	125.0	Released	7.292	2420.0	1.680000e+08
1	1	724495	The Woman King	Action-Drama-History	en	4957.725	TriStar Pictures-Entertainment One-JuVee Produ...	2022-09-15	5.000000e+07	9.100000e+07	135.0	Released	7.906	586.0	4.100000e+07
2	2	829799	Paradise City	Crime-Action-Thriller	en	3133.802	Arcana Studio-308 Enterprises-Yale Productions...	2022-11-11	2.000000e+07	1.647994e+07	93.0	Released	6.153	36.0	-3.520059e+06
3	3	792775	Cop Secret	Comedy-Action-Thriller	is	2447.908	Pegasus Pictures-Stöð 2-SamFilm	2022-05-23	6.066852e+06	1.647994e+07	100.0	Released	6.375	28.0	1.041309e+07
4	5	505642	Black Panther: Wakanda Forever	Action-Adventure-Science Fiction	en	2248.449	Marvel Studios	2022-11-09	2.500000e+08	7.330000e+08	162.0	Released	7.547	1182.0	4.830000e+08
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
43829	584853	174323	G.B.F.	Comedy-Drama	en	0.600	School Pictures-Parting Shots Media-Logolite E...	2013-04-19	3.200000e+06	1.647994e+07	93.0	Released	6.000	366.0	1.327994e+07
43830	587770	182219	Serial Teachers	Comedy	fr	0.600	UGC	2013-04-17	1.200000e+07	1.647994e+07	88.0	Released	5.470	1109.0	4.479941e+06
43831	609313	184374	Cinco de Mayo: The Battle	War-History-Drama	es	0.600	Estudios Churubusco Azteca-Gala Films-Gobierno...	2013-05-03	1.000000e+07	1.647994e+07	125.0	Released	7.700	67.0	6.479941e+06
43832	687240	510819	Dirty Dead Con Men	Action-Crime-Drama	en	0.600	Rock n' Tape Films-Thunder Alley Productions-N...	2018-03-30	6.066852e+06	1.647994e+07	85.0	Released	4.000	20.0	1.041309e+07
43833	711541	505039	Illicit Desires	Thriller	en	0.600	Retromedia Entertainment	2018-04-03	6.066852e+06	1.647994e+07	81.0	Released	4.500	20.0	1.041309e+07

	index	id	title	genres	original_language	popularity	production_companies	budget	revenue	runtime	imdb_ratings	vote_count	Profit	Profit_Percentage	year
40444	77710	571278	Scooby-Doo's Original Mysteries	Animation-Adventure	en	2.538	Hanna-Barbera Productions	6.066852e+06	1.647994e+07	110.0	10.0	23.0	1.041309e+07	171.639059	2000
23432	28815	392622	What's New, Scooby-Doo? Vol. 7: Ready to Scare	Animation-Comedy-Family-Mystery	en	6.206	Hanna-Barbera Productions-Cartoon Network Studios	6.066852e+06	1.647994e+07	97.0	10.0	42.0	1.041309e+07	171.639059	2006
39167	69069	638440	Scooby-Doo: Agence toutou risques, vol. 2 : Le...	Animation-Family-Comedy	fr	2.819	Hanna-Barbera Productions	6.066852e+06	1.647994e+07	97.0	10.0	21.0	1.041309e+07	171.639059	2007
35498	53109	495686	What's New Scooby-Doo? Vol. 4: Merry Scary Hol...	Animation-Comedy-Science Fiction-Action-Family	en	3.558	Warner Bros. Pictures-Hanna-Barbera Production...	6.066852e+06	1.647994e+07	85.0	10.0	41.0	1.041309e+07	171.639059	2007
36231	55602	609737	The 1st 13th Annual Fancy Anvil Awards Show Pr...	Animation-Family-Comedy	en	3.414	Cartoon Network-Hanna-Barbera Productions-Cart...	6.066852e+06	1.647994e+07	120.0	10.0	35.0	1.041309e+07	171.639059	2002
21769	26422	414119	What's New Scooby-Doo? Vol. 3: Halloween Boos ...	Animation-Comedy-Family-Mystery	en	6.702	Hanna-Barbera Productions-Cartoon Network Studios	6.066852e+06	1.647994e+07	84.0	10.0	44.0	1.041309e+07	171.639059	2007
42131	96325	638443	Scooby-Doo: Agence toutou risques, vol. 1 : Le...	Animation-Family-Comedy	fr	2.064	Hanna-Barbera Productions	6.066852e+06	1.647994e+07	97.0	10.0	21.0	1.041309e+07	171.639059	2007
34409	49786	405794	Cartoon Network Christmas: Yuletide Follies	Animation-Family-Comedy	en	3.765	Turner Home Entertainment-Cartoon Network-Hann...	6.066852e+06	1.647994e+07	110.0	9.9	40.0	1.041309e+07	171.639059	2004
40842	80914	642488	Mickey's Safety Club: Street Safe, Street Smart	Family-Animation-Comedy-Music-TV Movie	en	2.440	Disney Educational Productions-Walt Disney Stu...	6.066852e+06	1.647994e+07	13.0	9.9	30.0	1.041309e+07	171.639059	1989
25357	31600	386024	What's New, Scooby-Doo? Vol. 7: Ghosts on the Go!	Animation-Comedy-Family-Mystery	en	5.710	Warner Bros. Pictures	6.066852e+06	1.647994e+07	87.0	9.9	42.0	1.041309e+07	171.639059	2006

Data Cleaning¶